Garment Recovery with Shape and Deformation Priors
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916156432973824 |
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| author | Li, Ren Dumery, Corentin Guillard, Benoît Fua, Pascal |
| author_facet | Li, Ren Dumery, Corentin Guillard, Benoît Fua, Pascal |
| contents | While modeling people wearing tight-fitting clothing has made great strides in recent years, loose-fitting clothing remains a challenge. We propose a method that delivers realistic garment models from real-world images, regardless of garment shape or deformation. To this end, we introduce a fitting approach that utilizes shape and deformation priors learned from synthetic data to accurately capture garment shapes and deformations, including large ones. Not only does our approach recover the garment geometry accurately, it also yields models that can be directly used by downstream applications such as animation and simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_10356 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Garment Recovery with Shape and Deformation Priors Li, Ren Dumery, Corentin Guillard, Benoît Fua, Pascal Computer Vision and Pattern Recognition While modeling people wearing tight-fitting clothing has made great strides in recent years, loose-fitting clothing remains a challenge. We propose a method that delivers realistic garment models from real-world images, regardless of garment shape or deformation. To this end, we introduce a fitting approach that utilizes shape and deformation priors learned from synthetic data to accurately capture garment shapes and deformations, including large ones. Not only does our approach recover the garment geometry accurately, it also yields models that can be directly used by downstream applications such as animation and simulation. |
| title | Garment Recovery with Shape and Deformation Priors |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.10356 |